# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # TODO: Address all TODOs and remove all explanatory comments """Liv4ever dataset.""" import csv import json import os import datasets # TODO: Add BibTeX citation # Find for instance the citation on arxiv or on the dataset repo/website _CITATION = """\ @inproceedings{rikters-etal-2022, title = "Machine Translation for Livonian: Catering for 20 Speakers", author = "Rikters, Matīss and Tomingas, Marili and Tuisk, Tuuli and Valts, Ernštreits and Fishel, Mark", booktitle = "Proceedings of ACL 2022", year = "2022", address = "Dublin, Ireland", publisher = "Association for Computational Linguistics" } """ # TODO: Add description of the dataset here # You can copy an official description _DESCRIPTION = """\ Livonian is one of the most endangered languages in Europe with just a tiny handful of speakers and virtually no publicly available corpora. In this paper we tackle the task of developing neural machine translation (NMT) between Livonian and English, with a two-fold aim: on one hand, preserving the language and on the other – enabling access to Livonian folklore, lifestories and other textual intangible heritage as well as making it easier to create further parallel corpora. We rely on Livonian's linguistic similarity to Estonian and Latvian and collect parallel and monolingual data for the four languages for translation experiments. We combine different low-resource NMT techniques like zero-shot translation, cross-lingual transfer and synthetic data creation to reach the highest possible translation quality as well as to find which base languages are empirically more helpful for transfer to Livonian. The resulting NMT systems and the collected monolingual and parallel data, including a manually translated and verified translation benchmark, are publicly released. Fields: - source: source of the data - en: sentence in English - liv: sentence in Livonian """ # TODO: Add a link to an official homepage for the dataset here _HOMEPAGE = "https://huggingface.co/datasets/tartuNLP/liv4ever" # TODO: Add the licence for the dataset here if you can find it _LICENSE = "CC BY-NC-SA 4.0" # TODO: Add link to the official dataset URLs here # The HuggingFace Datasets library doesn't host the datasets but only points to the original files. # This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method) _URL = "https://huggingface.co/datasets/tartuNLP/liv4ever/raw/main/" _URLS = { "train": _URL + "train.json", "dev": _URL + "dev.json", "test": _URL + "test.json", } # TODO: Name of the dataset usually match the script name with CamelCase instead of snake_case class liv4ever(datasets.GeneratorBasedBuilder): """Liv4ever dataset.""" VERSION = datasets.Version("1.0.0") # This is an example of a dataset with multiple configurations. # If you don't want/need to define several sub-sets in your dataset, # just remove the BUILDER_CONFIG_CLASS and the BUILDER_CONFIGS attributes. # If you need to make complex sub-parts in the datasets with configurable options # You can create your own builder configuration class to store attribute, inheriting from datasets.BuilderConfig # BUILDER_CONFIG_CLASS = MyBuilderConfig # You will be able to load one or the other configurations in the following list with # data = datasets.load_dataset('my_dataset', 'train') # data = datasets.load_dataset('my_dataset', 'dev') # BUILDER_CONFIGS = [ # datasets.BuilderConfig(name="train", version=VERSION, description="This part of my dataset covers a first domain"), # datasets.BuilderConfig(name="dev", version=VERSION, description="This part of my dataset covers a second domain"), # ] # DEFAULT_CONFIG_NAME = "train" # It's not mandatory to have a default configuration. Just use one if it make sense. def _info(self): # TODO: This method specifies the datasets.DatasetInfo object which contains informations and typings for the dataset features = datasets.Features( { "source": datasets.Value("string"), "en": datasets.Value("string"), "liv": datasets.Value("string") # These are the features of your dataset like images, labels ... } ) return datasets.DatasetInfo( # This is the description that will appear on the datasets page. description=_DESCRIPTION, # This defines the different columns of the dataset and their types features=features, # Here we define them above because they are different between the two configurations # If there's a common (input, target) tuple from the features, uncomment supervised_keys line below and # specify them. They'll be used if as_supervised=True in builder.as_dataset. # supervised_keys=("sentence", "label"), # Homepage of the dataset for documentation homepage=_HOMEPAGE, # License for the dataset if available license=_LICENSE, # Citation for the dataset citation=_CITATION, ) def _split_generators(self, dl_manager): # TODO: This method is tasked with downloading/extracting the data and defining the splits depending on the configuration # If several configurations are possible (listed in BUILDER_CONFIGS), the configuration selected by the user is in self.config.name # dl_manager is a datasets.download.DownloadManager that can be used to download and extract URLS # It can accept any type or nested list/dict and will give back the same structure with the url replaced with path to local files. # By default the archives will be extracted and a path to a cached folder where they are extracted is returned instead of the archive # urls = _URLS[self.config.name] # data_dir = dl_manager.download_and_extract(urls) # return [ # datasets.SplitGenerator( # name=datasets.Split.TRAIN, # # These kwargs will be passed to _generate_examples # gen_kwargs={ # "filepath": os.path.join(data_dir), # "split": "train", # }, # ), # datasets.SplitGenerator( # name=datasets.Split.TEST, # # These kwargs will be passed to _generate_examples # gen_kwargs={ # "filepath": os.path.join(data_dir), # "split": "test" # }, # ), # datasets.SplitGenerator( # name=datasets.Split.VALIDATION, # # These kwargs will be passed to _generate_examples # gen_kwargs={ # "filepath": os.path.join(data_dir), # "split": "dev", # }, # ), # ] urls_to_download = _URLS downloaded_files = dl_manager.download_and_extract(urls_to_download) return [ datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": downloaded_files["train"]}), datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepath": downloaded_files["dev"]}), datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": downloaded_files["test"]}), ] # method parameters are unpacked from `gen_kwargs` as given in `_split_generators` def _generate_examples(self, filepath): # TODO: This method handles input defined in _split_generators to yield (key, example) tuples from the dataset. # The `key` is for legacy reasons (tfds) and is not important in itself, but must be unique for each example. with open(filepath, encoding="utf-8") as f: jsondata = json.load(f) n = 0 for source in jsondata: for sentence in source["sentences"]: # Yields examples as (key, example) tuples n=n+1 if source["source"] == "facebook" or source["source"] == "satversme": yield n, { "source": source["source"], "liv": sentence["liv"], "lv": sentence["lv"], "fr": sentence["fr"], "en": sentence["en"], } if source["source"] == "songs": yield n, { "source": source["source"], "liv": sentence["liv"], "lv": sentence["lv"], "et": sentence["et"], "en": sentence["en"], } if source["source"] == "trilium" or source["source"] == "dictionary" or source["source"] == "stalte": yield n, { "source": source["source"], "liv": sentence["liv"], "lv": sentence["lv"], "et": sentence["et"], } if source["source"] == "vaari" or source["source"] == "luule": yield n, { "source": source["source"], "liv": sentence["liv"], "et": sentence["et"], } if source["source"] == "jeful" and "et" in sentence: yield n, { "source": source["source"], "liv": sentence["liv"], "et": sentence["et"], } if source["source"] == "jeful" and "en" in sentence: yield n, { "source": source["source"], "liv": sentence["liv"], "en": sentence["en"], }